The field of the invention relates generally to disease diagnostics. More particularly, the invention relates to methods and systems for analyzing images of biologic cells to aid in identifying and classifying disease. In one embodiment of the present invention, the color space of an image of blood cells is transformed to create an alternate presentation of the image that more clearly identifies pathologic cells.
Pathology is an essential tool used in the diagnosis of various diseases. Typically, a licensed pathologist will observe a biological sample under a microscope to make a determination of whether a disease is present in the patient. The diagnosis is dependent on the skill and experience of the pathologist, the stage of disease progression, and the quality of the image captured. While pathology is commonly used, it is relatively expensive compared to other medical costs and not readily ordered in the early stages of a disease. For example, a patient who has developed cancer may exhibit symptoms consistent with more common diseases, such as the flu. Only when the patient has not responded to treatment of the symptoms might a treating physician perform a tissue analysis. However, in the example of a cancer patient, early detection improves the chance of success for treatment. In addition, the pathologist has a limited ability to quantify the abnormalities present in a sample, which could be critical in borderline cases.
To overcome these limitations, a number of approaches have been taken to perform computer vision-based and machine learning-based analysis of biologic cells as an aid in disease diagnostic. These methods seek to segment, or isolate, cell populations as a first step, then classify individual cells through further analysis as being abnormal or indicative of a disease. To perform computer vision-based analysis, various parameters of the image, such as color, intensity, or hue, are extracted from an image for processing by a computer.
As with most computer-vision based system, accurate segmentation is a critical step before analyzing the image. Image segmentation groups pixels within an image into regions or categories, where each region can correspond to an object in the image. Each pixel of a region has similar characteristics, or features, as other pixels in the region.
One of the simplest, and most predominant, cell segmentation approaches is intensity thresholding. In this image processing technique, global or local information in an image is utilized to compare the intensity of pixel to a threshold value. If a pixel is above the threshold, it is assigned to one category. Pixels below the threshold are assigned to a second category.
The intensity thresholding approach makes use of the assumption that cells and non-cells (background) have starkly different intensities and can be divided into separate categories. In practice, the illumination, color representation, and other image characteristics are dependent on the image capture device and will differ depending on the type and quality of the capture device used. Due to these differences, the intensity of cells and non-cells can be muted and the thresholding intensity assumption breaks down. As a result, the thresholding approach used alone gives poor segmentation results.
Instead of using the absolute intensity property alone, feature-based segmentation using filtering is also a common approach in cell segmentation. In one approach, filtering makes use of additional image characteristics to compare pixel intensity changes that can be used to identify the edge, for example, of an object in the image. Cells not separated by an edge are grouped into the same category. This approach provides useful cues but cannot give perfect cell segmentation results without further enhancements.
In addition to differential filters, which identify differences between two regions of an image, morphological filters using nonlinear operators such as erosion, dilation, opening and closing are also useful for cell segmentation. This approach is useful to enhance the image structure for segmentation by grouping neighboring pixels that have similar features. Region based segmentation using some primary knowledge or assumptions about initial points (seeds) and region growing is also very popular. Some common methods that use this approach are the hierarchical split-and-merge and watershed methods.
Another well-known approach is based on using deformable models, which are formulated as either implicit or explicit. Level sets, which are one of the most popular methods in this category, are able to handle topological changes and are thus useful for cell segmentation and cell tracking. However, most of the current approaches make use of global information when considering the entire image as a whole and do not give much consideration to the special features of blood cells, e.g that two different colors can be represented in the same cell, especially white blood cells.
Some approaches use a more targeted analysis of areas of color in a cell. Color space or color modeling is defined as a model that is able to represent color numerically in terms of three or more coordinates. Some common color spaces, such as RGB, YIQ, HSV, Lab, have been effectively used in many computer vision applications. However, such color spaces were not particularly designed for medical images and it shows some weakness when displaying a white blood cell, as shown in FIG. 1.
In FIG. 1, the first column shows the original image. The second column shows the image adapted to the HSI color space, in which the white blood cells are presented similarly to the background, creating little differentiation between the two. The third column shows the image in the RGB color space and the white blood cell is presented in a similar color to the red blood cell, which leads to difficulty in separating the two. Hence, finding an appropriate color space for peripheral blood images to present the white blood cells as a distinct component of the image is an importance task for pathological analysis of blood cells.
In addition to the drawbacks associated with current analysis techniques, most blood cell analysis methods and systems have concentrated on segmentation with the assumption that white blood cells are already present in the peripheral blood image. These methods have worked well on the images where white blood cells are present; however, the approaches experience difficulties when there are only red blood cells in the image, as shown in the first two columns of FIG. 2. For example, when no white blood cells are present, only two regions of the image, background and red blood cells, have to be separated. On the other hand, when white blood cells are present, three regions of the image, background, red blood cells, and white blood cells, have to be considered.
Given the drawbacks of current cell segmentation techniques, it is difficult to isolate and identify individual cells in an image. It would therefore be advantageous to develop a system and method of transforming a blood smear image to provide for a clear differentiation of targeted areas, enabling accurate segmentation of individual cells. With proper segmentation of the cells, classification of characteristics of individual cells can be used in disease diagnosis. While one embodiment of the present invention applies to blood cell images, the invention can be applied to images of other biologic cells.